Chen, Kewei

Background Retinal vascular imaging offers a non‐invasive window into the central nervous system health and has shown promise in the study of neurodegenerative diseases. Emerging evidence highlights that Parkinson's disease (PD) exhibits structural and functional changes in the retina, including vascular alterations. This pilot study evaluated whether retinal vascular morphological features, quantified using the deep learning tool Automorph, discriminate PD from non‐PD subjects in the UK Biobank cohort. Methods We analyzed color fundus photographs (CFPs) from 303 PD participants (mean age 62.6 ± 5.4 years, hypertension (HTN) = 67%, diabetes mellitus (DM) = 13%) and 516 non‐PD controls (age = 55.9±8.1, HTN = 22.8%, DM = 3%) enrolled in the UK Biobank. Six retinal vascular morphology features were quantified: fractal dimension, vessel density, average width, distance tortuosity, squared curvature tortuosity, and tortuosity density. General linear models were used to assess group differences while adjusting for age, hypertension, and diabetes. We also evaluated the association between retinal features and age, and the left‐right eye retinal fractal measures correlations using Pearson correlation and Fisher Z‐test. Results Compared to non‐PD individuals, PD patients showed significantly altered retinal vessel average width, both without (p = 0.001) and with (p = 0.002) vascular covariate adjustment. Group‐by‐age interaction effects were also significant for fractal dimension (p = 0.010) and vessel density (p = 0.023). Furthermore, inter‐eye correlation of vessel density differed significantly between groups (p = 0.034), suggesting altered bilateral vascular symmetry in PD. Conclusion Measurable alterations in retinal vascular morphology in PD support the potential of retina vascular imaging for non‐invasive neurodegenerative disease research. These results warrant further validation in larger cohorts, with improved analytic robustness and automated retinal vascular grading.

Background The association between lifestyle factors and Alzheimer's disease (AD) pathophysiology remains incompletely understood. Objective The aim of this study was to assess the association of alcohol consumption, smoking behavior, sleep quality and physical, cognitive, and social activity with cerebral amyloid pathology. Methods For this cross-sectional study, we selected participants from the Amyloid Biomarker Study data pooling initiative. We used generalized estimating equations to assess associations of dichotomized lifestyle measures with amyloid pathology. Results We included 9171 participants with normal cognition (NC) and 2555 participants with mild cognitive impairment (MCI) from the Amyloid Biomarker Study. Of participants with NC, 58% were women, 34% were APOE ε4 carrier, and 27% had amyloid pathology. Of participants with MCI, 48% were women, 47% were APOE ε4 carrier, and 57% had amyloid pathology. In NC, cognitively active participants were less likely to have amyloid pathology (OR = 0.77, 95%CI 0.66–0.89, p < 0.001). In MCI, participants who had ever smoked or had sleep problems were less likely to have amyloid pathology (OR = 0.85, 95%CI 0.73–0.99, p = 0.029; OR = 0.62, 95%CI 0.45–0.86, p = 0.004). Conclusions In NC, cognitive activity was associated with a lower frequency of amyloid pathology. In MCI, favorable lifestyle behaviors were not associated with a lower frequency of amyloid pathology. The results of the current study contribute to the broader evidence base on lifestyle and AD by further characterizing the role of lifestyle behaviors in AD pathology across different clinical stages.

Abstract Repetitive head impacts are associated with structural brain changes and an increased risk for chronic traumatic encephalopathy, a progressive neurodegenerative disease that can only be diagnosed after death. Chronic traumatic encephalopathy is defined by the abnormal accumulation of phosphorylated tau protein, particularly at the depths of the superior frontal sulci, suggesting that sulcal morphology may serve as a relevant structural biomarker. Contact sport athletes, such as former football players, are at elevated risk due to their prolonged exposure to repetitive head impacts. Cortical atrophy linked to underlying tau accumulation may result in shallower and wider sulci, potentially making sulcal morphology an imaging marker for identifying individuals at risk for this disease. This study investigated sulcal morphological differences in former football players and examined associations with age, football-related exposure, clinical diagnosis of traumatic encephalopathy syndrome, levels of certainty for chronic traumatic encephalopathy pathology, neuropsychological performance, and positron emission tomography imaging using flortaucipir. We analysed structural magnetic resonance imaging data from 169 male former football players (mean age 57.2 (8.2) years, range 45–74) and 54 age-matched, unexposed asymptomatic male controls (mean age 59.4 (8.5) years, range 45–74). Sulcal depth and width were quantified using the CalcSulc, focusing on two regions in each hemisphere commonly affected by chronic traumatic encephalopathy pathology: the superior frontal and occipitotemporal sulci. Generalized least squares models were used to assess group differences and interactions with age and football exposure variables, including age of first exposure, total years played, and cumulative head impact exposure. An analysis of covariance evaluated relationships between sulcal morphology, clinical measures, and flortaucipir uptake, adjusting for age, race, body mass index, education, imaging site, apolipoprotein E4 status, and total intracranial volume. Former football players demonstrated significantly shallower sulcal depth in the left superior frontal sulcus compared to unexposed controls. Earlier age of first exposure and longer football careers were associated with greater widening of the left occipitotemporal sulcus. Higher cumulative head impact exposure was linked to reduced sulcal depth in the left superior frontal region. However, sulcal morphology was not associated with clinical diagnosis, levels of certainty, neuropsychological test performance, or flortaucipir imaging. These findings suggest that sulcal morphology may reflect cumulative exposure to repetitive head impacts, particularly in brain regions vulnerable to chronic traumatic encephalopathy pathology. Future ante- and post-mortem validation studies are needed to determine whether sulcal morphology can serve as a reliable in vivo biomarker of risk.

Importance Tau positron emission tomography (PET) allows in vivo detection of neurofibrillary tangles, a core neuropathologic feature of Alzheimer disease (AD). Objective To provide estimates of the frequency of tau PET positivity and its associated risk of clinical outcomes. Design, Setting, and Participants Longitudinal study using data pooled from 21 cohorts, comprising a convenience sample of 6514 participants from 13 countries, collected between January 2013 and June 2024. Cognitively unimpaired individuals and patients with a clinical diagnosis of mild cognitive impairment (MCI), AD dementia, or other neurodegenerative disorders were included. Exposures Tau PET with flortaucipir F 18, amyloid-β (Aβ) PET, and clinical examinations. Tau PET scans were visually rated as positive according to a US Food and Drug Administration– and European Medicines Agency–approved method, designed to indicate the presence of advanced neurofibrillary tangle pathology (Braak stages V-VI). Main Outcomes and Measures Frequency of tau PET positivity and absolute risk of clinical progression (eg, progression to MCI or dementia). Results Among the 6514 participants (mean age, 69.5 years; 50.5% female), median follow-up time ranged from 1.5 to 4.0 years. Of 3487 cognitively unimpaired participants, 349 (9.8%) were tau PET positive; the estimated frequency of tau PET positivity was less than 1% in those aged younger than 50 years, and increased from 3% (95% CI, 2%-4%) at 60 years to 19% (95% CI, 16%-24%) at 90 years. Tau PET positivity frequency estimates increased across MCI and AD dementia clinical diagnoses (43% [95% CI, 41%-46%] and 79% [95% CI, 77%-82%] at 75 years, respectively). Most tau PET–positive individuals (92%) were also Aβ PET positive. Cognitively unimpaired participants who were positive for both Aβ PET and tau PET had a higher absolute risk of progression to MCI or dementia over the following 5 years (57% [95% CI, 45%-71%]) compared with both Aβ PET–positive/tau PET–negative (17% [95% CI, 13%-22%]) and Aβ PET–negative/tau PET–negative (6% [95% CI, 5%-8%]) individuals. Among participants with MCI at the time of the tau PET scan, an Aβ PET–positive/tau PET–positive profile was associated with a 5-year absolute risk of progression to dementia of 70% (95% CI, 59%-81%). Conclusions and Relevance In a large convenience sample, a positive tau PET scan occurred at a nonnegligible rate among cognitively unimpaired individuals, and the combination of Aβ PET positivity and tau PET positivity was associated with a high risk of clinical progression in both preclinical and symptomatic stages of AD. These findings underscore the potential of tau PET as a biomarker for staging AD pathology.

Importance Depressive symptoms are associated with cognitive decline in older individuals. Uncertainty about underlying mechanisms hampers diagnostic and therapeutic efforts. This large-scale study aimed to elucidate the association between depressive symptoms and amyloid pathology. Objective To examine the association between depressive symptoms and amyloid pathology and its dependency on age, sex, education, and APOE genotype in older individuals without dementia. Design, Setting, and Participants Cross-sectional analyses were performed using data from the Amyloid Biomarker Study data pooling initiative. Data from 49 research, population-based, and memory clinic studies were pooled and harmonized. The Amyloid Biomarker Study has been collecting data since 2012 and data collection is ongoing. At the time of analysis, 95 centers were included in the Amyloid Biomarker Study. The study included 9746 individuals with normal cognition (NC) and 3023 participants with mild cognitive impairment (MCI) aged between 34 and 100 years for whom data on amyloid biomarkers, presence of depressive symptoms, and age were available. Data were analyzed from December 2022 to February 2024. Main Outcomes and Measures Amyloid-β1-42 levels in cerebrospinal fluid or amyloid positron emission tomography scans were used to determine presence or absence of amyloid pathology. Presence of depressive symptoms was determined on the basis of validated depression rating scale scores, evidence of a current clinical diagnosis of depression, or self-reported depressive symptoms. Results In individuals with NC (mean [SD] age, 68.6 [8.9] years; 5664 [58.2%] female; 3002 [34.0%] APOE ε4 carriers; 937 [9.6%] had depressive symptoms; 2648 [27.2%] had amyloid pathology), the presence of depressive symptoms was not associated with amyloid pathology (odds ratio [OR], 1.13; 95% CI, 0.90-1.40; P = .29). In individuals with MCI (mean [SD] age, 70.2 [8.7] years; 1481 [49.0%] female; 1046 [44.8%] APOE ε4 carriers; 824 [27.3%] had depressive symptoms; 1668 [55.8%] had amyloid pathology), the presence of depressive symptoms was associated with a lower likelihood of amyloid pathology (OR, 0.73; 95% CI 0.61-0.89; P = .001). When considering subgroup effects, in individuals with NC, the presence of depressive symptoms was associated with a higher frequency of amyloid pathology in APOE ε4 noncarriers (mean difference, 5.0%; 95% CI 1.0-9.0; P = .02) but not in APOE ε4 carriers. This was not the case in individuals with MCI. Conclusions and Relevance Depressive symptoms were not consistently associated with a higher frequency of amyloid pathology in participants with NC and were associated with a lower likelihood of amyloid pathology in participants with MCI. These findings were not influenced by age, sex, or education level. Mechanisms other than amyloid accumulation may commonly underlie depressive symptoms in late life.

AbstractBackgroundCross‐modality translation between MRI and PET presents significant challenges due to the distinct mechanisms underlying those modalities. Currently, the leading method for this translation is CycleGAN. Previous studies have proved a strong correlation between BBBMs and brain amyloid status measured by PET. However, the impact of blood‐based biomarkers (BBBMs) on PET image synthesis has not yet been thoroughly evaluated. In this study, we propose Plasma CycleGAN, a generative model based on CycleGAN to synthesize PET images from MRI, while also incorporating BBBMs and other clinical covariates. We show that Plasma CycleGAN outperforms the state‐of‐the‐art CycleGAN for MRI‐to‐PET translation. Our method is the first to integrate BBBMs in a conditional cross modality translation from MRI to PET.MethodOur model was built on CycleGAN architecture, while the backbone was improved by integrating BBBMs as additional conditions in the input domain. To be specific, we introduce a masked perturbation in the MRI image, whose voxel‐level magnitudes are learnable parameters in the neural network. The dataset we used to evaluate our model is downloaded from the ADNI database. The cohort contains 160 subjects with matching BBBM, T1‐weighted MRI images, amyloid PET, and clinical features, including age, sex, weights, etc. The synthesized PET images are compared with the ground truth PET images for performance evaluation using traditional metrics like structural similarity index (SSIM), peak‐signal‐to‐noise ratio (PSNR) and mean square error (MSE). In addition, the Pearson correlation coefficients (PCCs) of the masked SUVR ratios were calculated.ResultWe evaluated the baseline CycleGAN model using MRI input, as well as combined inputs of MRI+BBBM and other clinical features. Our model achieved an SSIM of 0.72±0.16 and PSNR of 22.8±2.3 in PET image synthesis task using MRI+ Aβ42/40, both yielding the best results among all input combinations. MRI+sex achieved the best result using MSE measurement. PCCs of all experimental results ranged from 0.44 to 0.5.ConclusionWe introduce Plasma CycleGAN, the first generative model to integrate BBBMs and clinical features in PET image synthesis from MRI images. In PET image synthesis task, our proposed integrated model outperforms the baseline CycleGAN model.

AbstractBackgroundLewy body (LB) diseases can present with overlapping prodromal, cognitive, motor, autonomic or neuropsychiatric symptoms. Intuitively, greater symptom severity should correlate with greater pathological burden, but this has not been consistently shown. LB pathology does not translate to clinical expression in Incidental LB disease. While several composite scores/toolkits utilize overlapping schemes for diagnosis, a composite data‐derived score to predict LB pathologic burden is lacking. We aimed to utilize an autopsy‐confirmed cohort, and validated, brief, and standardized clinical, functional, pathologic assessments, and machine learning (ML)/quantitative modeling techniques to identify clusters and importance rankings among assessments/measures to predict LB pathologic severity and density.MethodA total of 234 subjects with neuropathological finding of LB at autopsy in the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND) who were classified as Cognitively Unimpaired or Mild Cognitive Impairment after their first study clinical evaluation were included. Of these, 145 subjects had complete scores for 32 of 46 measures (clinical, cognitive, behavioral and functional) that were utilized as predictors of LB pathology. Olfactory function was assessed using the University of Pennsylvania Smell Identification Test (UPSIT). LB severity was assessed by the Unified Staging System for Lewy Body Disorders (USSLB). Several ML algorithms and quantitative prediction models were explored and compared: artificial neural network (ANN), partial least square regression (PLSR), support vector regression (SVR), relevance vector regression (RVR) and ensemble forest regression (EFR) with leave‐one‐out (LOO) scheme. Game‐theory based Shapley methods assessed the impact, including rankings, consistency and magnitude, of model predictors.ResultRVR predicted aggregate LB density with large effect size (R2 = 0.691, p<0.00001). ANN predicted USSLB severity stage with large effect size (R2 = 0.74, p<2.5e‐43). All other ML algorithms/models provided substantial prediction. Across all models, UPSIT was the most influential predictor (>65%), followed by Controlled Oral Word Association Test (COWAT) and age.ConclusionThese preliminary and exploratory results support the utilization of ML techniques/models to assess LB pathologic burden with key measures collected in relatively small samples. UPSIT was consistently ranked highest impactful among clinical and functional measures/predictors. Integrated together in ML/data‐derived composites, UPSIT, COWAT and other clinical characteristics may be of antemortem utility to predict USSLB stages.

ImportanceExposure to repetitive head impacts (RHI) is associated with increased risk for neurodegeneration. Accumulation of toxic proteins due to impaired brain clearance is suspected to play a role.ObjectiveTo investigate whether perivascular space (PVS) volume is associated with lifetime exposure to RHI in individuals at risk for RHI-associated neurodegeneration.Design, Setting, and ParticipantsThis cross-sectional study was part of the Diagnostics, Imaging, and Genetics Network for the Objective Study and Evaluation of Chronic Traumatic Encephalopathy (DIAGNOSE CTE) Research Project, a 7-year multicenter study consisting of 4 US study sites. Data were collected from September 2016 to February 2020 and analyses were performed between May 2021 and October 2023. After controlling for magnetic resonance image (MRI) and processing quality, former American football players and unexposed asymptomatic control participants were included in analyses.ExposurePrior exposure to RHI while participating in American football was estimated using the 3 cumulative head impact indices (CHII-G, linear acceleration; CHII-R, rotational acceleration; and CHII, number of head impacts).Main Outcomes and MeasuresIndividual PVS volume was calculated in the white matter of structural MRI. Cognitive impairment was based on neuropsychological assessment. Linear regression models were used to assess associations of PVS volume with neuropsychological assessments in former American football players. All analyses were adjusted for confounders associated with PVS volume.ResultsAnalyses included 224 participants (median [IQR] age, 57 [51-65] years), with 170 male former football players (114 former professional athletes, 56 former collegiate athletes) and 54 male unexposed control participants. Former football players had larger PVS volume compared with the unexposed group (mean difference, 0.28 [95% CI, 0.00-0.56]; P = .05). Within the football group, PVS volume was associated with higher CHII-R (β = 2.71 × 10−8 [95% CI, 0.50 × 10−8 to 4.93 × 10−8]; P = .03) and CHII-G (β = 2.24 × 10−6 [95% CI, 0.35 × 10−6 to 4.13 × 10−6]; P = .03). Larger PVS volume was also associated with worse performance on cognitive functioning in former American football players (β = −0.74 [95% CI, −1.35 to −0.13]; P = .04).Conclusions and RelevanceThese findings suggest that impaired perivascular brain clearance, as indicated by larger PVS volume, may contribute to the association observed between RHI exposure and neurodegeneration.